Factors associated with fall risk of community-dwelling older people: A decision tree analysis

ObjectiveTo examine the predictive attributes for accidental falls in community-dwelling older people in Hong Kong using decision tree analysis. MethodsWe recruited 1151 participants with an average age of 74.8 years by convenience sampling from a primary healthcare setting to carry out the cross-sectional study over 6 months. The whole dataset was divided into two sets, namely training set and test set, which respectively occupied 70% and 30% of the whole dataset. The training dataset was used first; decision tree analysis was used to identify possible stratifying variables that could help to generate separate decision models. ResultsThe number of fallers was 230 with 20% 1-year prevalence. There were significant differences in gender, use of walking aids, presence of chronic diseases, and co-morbidities including osteoporosis, depression, and previous upper limb fractures, and performance in the Timed Up and Go test and the Functional Reach test among the baselines between the faller and non-faller groups. Three decision tree models for the dependent dichotomous variables (fallers, indoor fallers, and outdoor fallers) were generated, with overall accuracy rates of the models of 77.40%, 89.44% and 85.76%, respectively. Timed Up and Go, Functional Reach, body mass index, high blood pressure, osteoporosis, and number of drugs taken were identified as stratifying variables in the decision tree models for fall screening. ConclusionThe use of decision tree analysis for clinical algorithms for accidental falls in community-dwelling older people creates patterns for decision-making in fall screening, which also paves the way for utility-based decision-making using supervised machine learning in fall risk detection.

Automated summary

This study aimed to identify predictive attributes for accidental falls in community-dwelling older people in Hong Kong using decision tree analysis. A cross-sectional study was conducted over 6 months, with 1151 participants recruited from a primary healthcare setting. The results showed that decision tree analysis created decision models for fall screening in older people and paved the way for utility-based decision-making using supervised machine learning in fall risk detection.